• Part 0: A little bit of set up!
• Part 1: reading CSV file, common new user mistakes in data reading, checking for problems in the read data
• Part 2: data input overview, working directories, relative vs. absolute paths, reading XLSX file (Excel file), other data inputs
• Part 3: writing CSV file
• Part 4: reading and saving R objects

Let’s make an R Project so we can stay organized in the next steps.

Click the new R Project button at the top left of RStudio:

In the New Project Wizard, click “New Directory”:

Click “New Project”:

Type in a name for your new folder.

Store it somewhere easy to find, such as your Desktop:

You now have a new R Project folder on your Desktop!

Make sure you add any scripts or data files to this folder as we go through today’s lesson. This will make sure R is able to “find” your files.

• Everything we do in class will be using real publicly available data - there are few ‘toy’ example datasets and ‘simulated’ data
• Baltimore Open Data and Data.gov will be sources for the first few days
• We have also added functionality to load these datasets directly in the jhur package

• ‘Reading in’ data is the first step of any real project/analysis
• R can read almost any file format, especially via add-on packages
• We are going to focus on simple delimited files first
  • comma separated (e.g. ‘.csv’)
  • tab delimited (e.g. ‘.txt’)
  • Microsoft Excel (e.g. ‘.xlsx’)

Youth Tobacco Survey (YTS) dataset:

“The YTS was developed to provide states with comprehensive data on both middle school and high school students regarding tobacco use, exposure to environmental tobacco smoke, smoking cessation, school curriculum, minors’ ability to purchase or otherwise obtain tobacco products, knowledge and attitudes about tobacco, and familiarity with pro-tobacco and anti-tobacco media messages.”

• Check out the data at: https://catalog.data.gov/dataset/youth-tobacco-survey-yts-data

Dataset is located at http://jhudatascience.org/intro_to_R_class/data/Youth_Tobacco_Survey_YTS_Data.csv

• Download data by clicking the above link

  • Safari - if a file loads in your browser, choose File –> Save As, select, Format “Page Source” and save

# load library `readr` that contains function `read_csv`
library(readr)
dat <- read_csv("http://jhudatascience.org/intro_to_R_class/data/Youth_Tobacco_Survey_YTS_Data.csv")

# `head` displays first few rows of a data frame 
head(dat, 5)

# A tibble: 5 x 31
   YEAR LocationAbbr LocationDesc TopicType  TopicDesc  MeasureDesc   DataSource
  <dbl> <chr>        <chr>        <chr>      <chr>      <chr>         <chr>     
1  2015 AZ           Arizona      Tobacco U… Cessation… Percent of C… YTS       
2  2015 AZ           Arizona      Tobacco U… Cessation… Percent of C… YTS       
3  2015 AZ           Arizona      Tobacco U… Cessation… Percent of C… YTS       
4  2015 AZ           Arizona      Tobacco U… Cessation… Quit Attempt… YTS       
5  2015 AZ           Arizona      Tobacco U… Cessation… Quit Attempt… YTS       
# … with 24 more variables: Response <chr>, Data_Value_Unit <chr>,
#   Data_Value_Type <chr>, Data_Value <dbl>, Data_Value_Footnote_Symbol <chr>,
#   Data_Value_Footnote <chr>, Data_Value_Std_Err <dbl>,
#   Low_Confidence_Limit <dbl>, High_Confidence_Limit <dbl>, Sample_Size <dbl>,
#   Gender <chr>, Race <chr>, Age <chr>, Education <chr>, GeoLocation <chr>,
#   TopicTypeId <chr>, TopicId <chr>, MeasureId <chr>, StratificationID1 <chr>,
#   StratificationID2 <chr>, StratificationID3 <chr>, StratificationID4 <chr>,
#   SubMeasureID <chr>, DisplayOrder <dbl>

So what is going on “behind the scenes”?

read_csv() parses a “flat” text file (.csv) and turns it into a tibble – a rectangular data frame, where data are split into rows and columns

• First, a flat file is parsed into a rectangular matrix of strings

• Second, the type of each column is determined (heuristic-based guess)

read_csv() needs the path to your file. It will return a tibble

read_csv(file, col_names = TRUE, col_types = NULL,
  locale = default_locale(), na = c("", "NA"),
  quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE,
  skip = 0, n_max = Inf, guess_max = min(1000, n_max),
  progress = show_progress(), skip_empty_rows = TRUE
)

• file is the path to your file, in quotes
• can be path in your local computer – absolute file path or relative file path
• can be path to a file on a website

## Examples 

dat <- read_csv("/Users/avahoffman/Downloads/Youth_Tobacco_Survey_YTS_Data.csv")

dat <- read_csv("Youth_Tobacco_Survey_YTS_Data.csv")

dat <- read_csv("www.someurl.com/table1.csv")

Great, but what is my “path”?

Luckily, we already set up an R Project!

If we add the Youth_Tobacco_Survey_YTS_Data.csv file to the intro_to_r_class folder, we can use the relative path:

dat <- read_csv("Youth_Tobacco_Survey_YTS_Data.csv")

read_csv() is a special case of read_delim() – a general function to read a delimited file into a data frame

read_delim() needs path to your file and fileds delimiter, will return a tibble

read_delim(file, delim, quote = "\"", escape_backslash = FALSE, 
  escape_double = TRUE, col_names = TRUE,  col_types = NULL, 
  locale = default_locale(),na = c("", "NA"),  quoted_na = TRUE, 
  comment = "", trim_ws = FALSE, skip = 0, 
  n_max = Inf,  guess_max = min(1000, n_max), 
  progress = show_progress(), skip_empty_rows = TRUE
)

• file is the path to your file, in quotes
• delim is what separates the fields within a record

## Examples 
dat <- read_delim("Youth_Tobacco_Survey_YTS_Data.csv", delim = ",")

dat <- read_delim("www.someurl.com/table1.txt", delim = "\t")

dat <- read_csv("../data/Youth_Tobacco_Survey_YTS_Data.csv")

── Column specification ────────────────────────────────────────────────────────
cols(
  .default = col_character(),
  YEAR = col_double(),
  Data_Value = col_double(),
  Data_Value_Std_Err = col_double(),
  Low_Confidence_Limit = col_double(),
  High_Confidence_Limit = col_double(),
  Sample_Size = col_double(),
  DisplayOrder = col_double()
)
ℹ Use `spec()` for the full column specifications.

The data is now successfully read into your R workspace. Colum specification of first few columns is printed to the console.

1. Working directory problems: trying to read files that R “can’t find”
   • Path misspecification
2. Typos (R is case sensitive, x and X are different)
   • RStudio helps with “tab completion”
3. Data type problems (is that a string or a number?)
4. Open ended quotes, parentheses, and brackets
   
5. Different versions of software

The spec() function shows you the specification of how the data was read in.

# dat <- read_csv("../data/Youth_Tobacco_Survey_YTS_Data.csv")
spec(dat)

cols(
  YEAR = col_double(),
  LocationAbbr = col_character(),
  LocationDesc = col_character(),
  TopicType = col_character(),
  TopicDesc = col_character(),
  MeasureDesc = col_character(),
  DataSource = col_character(),
  Response = col_character(),
  Data_Value_Unit = col_character(),
  Data_Value_Type = col_character(),
  Data_Value = col_double(),
  Data_Value_Footnote_Symbol = col_character(),
  Data_Value_Footnote = col_character(),
  Data_Value_Std_Err = col_double(),
  Low_Confidence_Limit = col_double(),
  High_Confidence_Limit = col_double(),
  Sample_Size = col_double(),
  Gender = col_character(),
  Race = col_character(),
  Age = col_character(),
  Education = col_character(),
  GeoLocation = col_character(),
  TopicTypeId = col_character(),
  TopicId = col_character(),
  MeasureId = col_character(),
  StratificationID1 = col_character(),
  StratificationID2 = col_character(),
  StratificationID3 = col_character(),
  StratificationID4 = col_character(),
  SubMeasureID = col_character(),
  DisplayOrder = col_double()
)

The problems() function shows you if there were any obvious issues when the data was read in.

The output of problems() is a tibble showing each line with a concern.

problems(dat)

[1] row      col      expected actual  
<0 rows> (or 0-length row.names)

dat looks good so far. What do you see on a messy dataset?

ufo_data <- read_csv("https://github.com/SISBID/Data-Wrangling/blob/gh-pages/data/ufo/ufo_data_complete.csv")

problems(ufo_data)

# A tibble: 73 x 5
     row col   expected  actual    file                                         
   <int> <chr> <chr>     <chr>     <chr>                                        
 1    98 <NA>  1 columns 2 columns 'https://github.com/SISBID/Data-Wrangling/bl…
 2   106 <NA>  1 columns 353 colu… 'https://github.com/SISBID/Data-Wrangling/bl…
 3   107 <NA>  1 columns 2 columns 'https://github.com/SISBID/Data-Wrangling/bl…
 4   140 <NA>  1 columns 3 columns 'https://github.com/SISBID/Data-Wrangling/bl…
 5   150 <NA>  1 columns 6 columns 'https://github.com/SISBID/Data-Wrangling/bl…
 6   171 <NA>  1 columns 4 columns 'https://github.com/SISBID/Data-Wrangling/bl…
 7   176 <NA>  1 columns 10 colum… 'https://github.com/SISBID/Data-Wrangling/bl…
 8   180 <NA>  1 columns 10 colum… 'https://github.com/SISBID/Data-Wrangling/bl…
 9   184 <NA>  1 columns 10 colum… 'https://github.com/SISBID/Data-Wrangling/bl…
10   188 <NA>  1 columns 10 colum… 'https://github.com/SISBID/Data-Wrangling/bl…
# … with 63 more rows

The stop_for_problems() function will stop if your data had any problem when reading in (even if that problem did not cause the data reading to fail).

• Particularly useful to put after the data reading code e.g. in some automated R script that should not proceed in case some data “weirdness” occurred.

stop_for_problems(ufo_data)

Error: 73 parsing failures

For any function, you can write ?FUNCTION_NAME, or help("FUNCTION_NAME") to look at the help file:

?read_delim
help("read_delim")

R Studio features some nice “drop-down” support, where you can run some tasks by selecting them from the toolbar.

For example, you can easily import text datasets using the File --> Import Dataset --> From Text (readr) command. Selecting this will bring up a new screen that lets you specify the formatting of your text file.

After importing a datatset, you get (printed in the R console) the corresponding R command that you can enter in the console if you want to re-import data.

There are also data importing functions provided in base R (rather than the readr package), like read.delim() and read.csv().

These functions have slightly different syntax for reading in data (e.g. header argument).

However, while many online resources use the base R tools, the latest version of RStudio switched to use these new readr data import tools, so we will use them in the class for slides. They are also up to two times faster for reading in large datasets, and have a progress bar which is nice.

• Data importing functions provided in base R: read.delim(), read.csv()

• Modern, improved tools from readr R package: read_delim(), read_csv()

  • needs a file path to be provided
  • parses the file into rows/columns, determines column type
  • returns a data frame

• Some functions to look at a data frame:

  • head() shows first few rows
  • spec() gives specification of column types

• From readr package:

  • read_delim(): general delimited files
  • read_csv(): comma separated (CSV) files
  • read_tsv(): tab separated files
  • others

• For reading Excel files, you can do one of:

  • open in Excel, “Save as” a sheet as a .csv file, and open using read_csv()
  • use read_excel() function from readxl package
  • use other packages: xlsx, openxlsx

• haven package has functions to read SAS, SPSS, Stata formats

• sas7bdat has functions to read SAS formats

Lab file: http://jhudatascience.org/intro_to_r_class/Data_IO/lab/Data_IO_Lab.Rmd

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Working directory is a directory that R assumes “you are working in”. It’s where R looks for files.

“Setting working directory” means specifying the path to the directory.

# get the working directory
getwd()

# set the working directory
setwd("/Users/avahoffman/Desktop") 

R uses working directory as a starting place when searching for files.

R uses working directory as a starting place when searching for files:

• if you use read_csv("Bike_Lanes_Long.csv"), R assumes that the file is in the working directory

• if you use read_csv("data/Bike_Lanes_Long.csv"), R assumes that data directory is in the working directory

• if you use an absolute path, e.g. read_csv("/Users/avahoffman/data/Bike_Lanes_Long.csv"), the working directory information is not used

Setting up an R Project can avoid headaches by telling R that the working directory is wherever the .Rproj file is.

While its nice to be able to read in a variety of data formats, it’s equally important to be able to output data somewhere.

The readr package provides data exporting functions which have the pattern write_*:

• write_csv(),
• write_delim(), others.

From write_csv() documentation:

write_csv(x, file, 
  na = "NA", append = FALSE, 
  col_names = !append, quote_escape = "double",
  eol = "\n", path = deprecated()
)

x: data frame you want to write

file: file path where you want to R object written; it can be:

• an absolute path,
• a relative path (relative to your working directory),
• a file name only (which writes the file to your working directory)

# Examples 

write_csv(dat, file = "YouthTobacco_newNames.csv")

write_delim(dat, file = "YouthTobacco_newNames.csv", delim = ",")

.rds is an extension for R native file format.

write_rds() and read_rds() from readr package can be used to write/read a single R variable to/from file.

Saving datasets in .rds format can save time if you have to read it back in later.

# write a variable: a data frame "dat" 
write_rds(dat, file = "yts_dataset.rds")

# write a variable: vector "x"
x <- c(1,3,3)
write_rds(x, file = "my_vector.rds")

# read a variable from file and assign to a new variable named "y"
x2 <- read_rds("my_vector.rds")
x2

[1] 1 3 3

Lab file: http://jhudatascience.org/intro_to_r_class/Data_IO/lab/Data_IO_Lab.Rmd

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